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Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings
[article]
2018
arXiv
pre-print
Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In the paper we present an approach that combines a lexical index, a neural embedding model and locality modules to effectively divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed methods are
arXiv:1805.12402v1
fatcat:knqwm6zhkjcs5agotdn6k5f6ny